
# To supress warnings
import warnings
warnings.filterwarnings("ignore")
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# to scale the data using z-score
from sklearn.preprocessing import StandardScaler

# to compute distances
from scipy.spatial.distance import cdist, pdist

# to perform k-means clustering and compute silhouette scores
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

# to perform hierarchical clustering, compute cophenetic correlation, and create dendrograms
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage, cophenet

# to perform PCA
from sklearn.decomposition import PCA

# to visualize the elbow curve and silhouette scores
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer

#format numeric data for easier readability
pd.set_option(
    "display.float_format", lambda x: "%.2f" % x
)  # to display numbers rounded off to 2 decimal places

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)


#function to print labeled barplots
def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 1, 5))
    else:
        plt.figure(figsize=(n + 1, 5))

    plt.xticks(rotation=90, fontsize=12)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="viridis",
        order=data[feature].value_counts().index[:n].sort_values(),
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  #width
        y = p.get_height()  # height 

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )


def histogram_boxplot(data, feature, figsize=(16, 6), kde=False, bins=None, hue=None):
    """
    Combines boxplot and histogram

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (16,6))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True,
    )  # boxplot will be created and a star will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins, palette="winter",
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )


# reading the csv file
trd = pd.read_csv('stock_data.csv')


#creating a copy of the file
td = trd.copy()


# let's view a sample of the data
td.sample(n=10, random_state= 1)


td.shape

(340, 15)


td.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 340 entries, 0 to 339
Data columns (total 15 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   Ticker Symbol                 340 non-null    object 
 1   Security                      340 non-null    object 
 2   GICS Sector                   340 non-null    object 
 3   GICS Sub Industry             340 non-null    object 
 4   Current Price                 340 non-null    float64
 5   Price Change                  340 non-null    float64
 6   Volatility                    340 non-null    float64
 7   ROE                           340 non-null    int64  
 8   Cash Ratio                    340 non-null    int64  
 9   Net Cash Flow                 340 non-null    int64  
 10  Net Income                    340 non-null    int64  
 11  Earnings Per Share            340 non-null    float64
 12  Estimated Shares Outstanding  340 non-null    float64
 13  P/E Ratio                     340 non-null    float64
 14  P/B Ratio                     340 non-null    float64
dtypes: float64(7), int64(4), object(4)
memory usage: 40.0+ KB


td.isnull().sum()

Ticker Symbol                   0
Security                        0
GICS Sector                     0
GICS Sub Industry               0
Current Price                   0
Price Change                    0
Volatility                      0
ROE                             0
Cash Ratio                      0
Net Cash Flow                   0
Net Income                      0
Earnings Per Share              0
Estimated Shares Outstanding    0
P/E Ratio                       0
P/B Ratio                       0
dtype: int64


td.duplicated().sum()

0


td.describe().T       #will generate a statistical summary of the dataset in a transpose form


td.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 340 entries, 0 to 339
Data columns (total 15 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   Ticker Symbol                 340 non-null    object 
 1   Security                      340 non-null    object 
 2   GICS Sector                   340 non-null    object 
 3   GICS Sub Industry             340 non-null    object 
 4   Current Price                 340 non-null    float64
 5   Price Change                  340 non-null    float64
 6   Volatility                    340 non-null    float64
 7   ROE                           340 non-null    int64  
 8   Cash Ratio                    340 non-null    int64  
 9   Net Cash Flow                 340 non-null    int64  
 10  Net Income                    340 non-null    int64  
 11  Earnings Per Share            340 non-null    float64
 12  Estimated Shares Outstanding  340 non-null    float64
 13  P/E Ratio                     340 non-null    float64
 14  P/B Ratio                     340 non-null    float64
dtypes: float64(7), int64(4), object(4)
memory usage: 40.0+ KB


for i in td.columns[td.dtypes=='object']:
    td[i] = td[i].astype('category')


td.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 340 entries, 0 to 339
Data columns (total 15 columns):
 #   Column                        Non-Null Count  Dtype   
---  ------                        --------------  -----   
 0   Ticker Symbol                 340 non-null    category
 1   Security                      340 non-null    category
 2   GICS Sector                   340 non-null    category
 3   GICS Sub Industry             340 non-null    category
 4   Current Price                 340 non-null    float64 
 5   Price Change                  340 non-null    float64 
 6   Volatility                    340 non-null    float64 
 7   ROE                           340 non-null    int64   
 8   Cash Ratio                    340 non-null    int64   
 9   Net Cash Flow                 340 non-null    int64   
 10  Net Income                    340 non-null    int64   
 11  Earnings Per Share            340 non-null    float64 
 12  Estimated Shares Outstanding  340 non-null    float64 
 13  P/E Ratio                     340 non-null    float64 
 14  P/B Ratio                     340 non-null    float64 
dtypes: category(4), float64(7), int64(4)
memory usage: 58.1 KB


td['Ticker Symbol'].nunique()

340


td.drop("Ticker Symbol", axis = 1 , inplace = True)


td.info()           #re-checking the updated dataset

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 340 entries, 0 to 339
Data columns (total 14 columns):
 #   Column                        Non-Null Count  Dtype   
---  ------                        --------------  -----   
 0   Security                      340 non-null    category
 1   GICS Sector                   340 non-null    category
 2   GICS Sub Industry             340 non-null    category
 3   Current Price                 340 non-null    float64 
 4   Price Change                  340 non-null    float64 
 5   Volatility                    340 non-null    float64 
 6   ROE                           340 non-null    int64   
 7   Cash Ratio                    340 non-null    int64   
 8   Net Cash Flow                 340 non-null    int64   
 9   Net Income                    340 non-null    int64   
 10  Earnings Per Share            340 non-null    float64 
 11  Estimated Shares Outstanding  340 non-null    float64 
 12  P/E Ratio                     340 non-null    float64 
 13  P/B Ratio                     340 non-null    float64 
dtypes: category(3), float64(7), int64(4)
memory usage: 46.7 KB


histogram_boxplot(td, 'Current Price')


histogram_boxplot(td, 'Price Change')


#A labeled barplot of stocks based on the sectors
labeled_barplot(td, 'GICS Sector')


#the top 5 leading sub-industries with the most amount of stocks. 
td['GICS Sector'].value_counts().head(5)

Industrials               53
Financials                49
Consumer Discretionary    40
Health Care               40
Information Technology    33
Name: GICS Sector, dtype: int64


#A labeled barplot of stocks based on the sub-industries
labeled_barplot(td, 'GICS Sub Industry')


#the top 3 leading sub-industries with the most amount of stocks. 
td['GICS Sub Industry'].value_counts().head(3)

Oil & Gas Exploration & Production    16
REITs                                 14
Industrial Conglomerates              14
Name: GICS Sub Industry, dtype: int64


histogram_boxplot(td,'Volatility')      #will generate the boxplot of Volatility


histogram_boxplot(td, 'Net Cash Flow')


histogram_boxplot(td, 'Earnings Per Share')


histogram_boxplot(td, 'Estimated Shares Outstanding')


histogram_boxplot(td, 'ROE')


histogram_boxplot(td, 'P/E Ratio')


histogram_boxplot(td, 'P/B Ratio')


histogram_boxplot(td , 'Net Income')


plt.figure(figsize=(15, 7))
sns.heatmap(
    td.corr(), annot=True, vmin=-1, vmax=1, fmt=".2f", cmap="Spectral"
)
plt.show()


td.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 340 entries, 0 to 339
Data columns (total 14 columns):
 #   Column                        Non-Null Count  Dtype   
---  ------                        --------------  -----   
 0   Security                      340 non-null    category
 1   GICS Sector                   340 non-null    category
 2   GICS Sub Industry             340 non-null    category
 3   Current Price                 340 non-null    float64 
 4   Price Change                  340 non-null    float64 
 5   Volatility                    340 non-null    float64 
 6   ROE                           340 non-null    int64   
 7   Cash Ratio                    340 non-null    int64   
 8   Net Cash Flow                 340 non-null    int64   
 9   Net Income                    340 non-null    int64   
 10  Earnings Per Share            340 non-null    float64 
 11  Estimated Shares Outstanding  340 non-null    float64 
 12  P/E Ratio                     340 non-null    float64 
 13  P/B Ratio                     340 non-null    float64 
dtypes: category(3), float64(7), int64(4)
memory usage: 46.7 KB


plt.figure(figsize=(15,8))
sns.barplot(data=td, x='GICS Sector', y='ROE', ci=False)
plt.xticks(rotation=90)
plt.show()


plt.figure(figsize=(15,8))
sns.barplot(data=td, x='GICS Sector', y='Cash Ratio', ci=False)  
plt.xticks(rotation=90)
plt.show()


td.groupby('GICS Sector')['Cash Ratio'].mean().sort_values(ascending=False)

GICS Sector
Information Technology        149.82
Telecommunications Services   117.00
Health Care                   103.78
Financials                     98.59
Consumer Staples               70.95
Energy                         51.13
Real Estate                    50.11
Consumer Discretionary         49.58
Materials                      41.70
Industrials                    36.19
Utilities                      13.62
Name: Cash Ratio, dtype: float64


plt.figure(figsize=(15,8))
sns.barplot(data=td, x='GICS Sector', y='P/E Ratio', ci=False) 
plt.xticks(rotation=90)
plt.show()


td.groupby('GICS Sector')['P/E Ratio'].mean().sort_values(ascending=False)

GICS Sector
Energy                        72.90
Information Technology        43.78
Real Estate                   43.07
Health Care                   41.14
Consumer Discretionary        35.21
Consumer Staples              25.52
Materials                     24.59
Utilities                     18.72
Industrials                   18.26
Financials                    16.02
Telecommunications Services   12.22
Name: P/E Ratio, dtype: float64


td.duplicated()

0      False
1      False
2      False
3      False
4      False
       ...  
335    False
336    False
337    False
338    False
339    False
Length: 340, dtype: bool


td.isnull().sum()

Security                        0
GICS Sector                     0
GICS Sub Industry               0
Current Price                   0
Price Change                    0
Volatility                      0
ROE                             0
Cash Ratio                      0
Net Cash Flow                   0
Net Income                      0
Earnings Per Share              0
Estimated Shares Outstanding    0
P/E Ratio                       0
P/B Ratio                       0
dtype: int64


plt.figure(figsize=(15, 12))

numeric_columns = td.select_dtypes(include=np.number).columns.tolist()

for i, variable in enumerate(numeric_columns):
    plt.subplot(3, 4, i + 1)
    plt.boxplot(td[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


#create list of columns with numerical variables
num_col = td.select_dtypes(include=np.number).columns.tolist()


#scale the data set before clustering
scaler = StandardScaler()
subset = td[num_col].copy()
subset_scaled = scaler.fit_transform(subset)


#create a dataframe from the scaled data
subset_scaled_td = pd.DataFrame(subset_scaled, columns=subset.columns)


#create pairplot for scaled dataframe
sns.pairplot(subset_scaled_td, height=2,aspect=2 , diag_kind='kde')
plt.show()


k_means_df = subset_scaled_td.copy()


clusters = range(1, 11)
# Creating an empty list to store average distortions for each cluster number
meanDistortions = []
# Iterating through the cluster numbers
for k in clusters:
    # Creating a KMeans clustering model with the current number of clusters (k)
    model = KMeans(n_clusters=k)
    model.fit(subset_scaled_td)
    # Predict cluster assignments for the entire dataset 'k_means_df'
    prediction = model.predict(k_means_df)
     # Calculates the distortion (average distance from each data point to its assigned cluster center)
    distortion = (
        sum(
            np.min(cdist(subset_scaled_td, model.cluster_centers_, "euclidean"), axis=1)
        )
        / subset_scaled_td.shape[0]
    )
    
    # Append the calculated average distortion to the 'meanDistortions' list
    meanDistortions.append(distortion)
    # Prints the current number of clusters and its corresponding average distortion
    print("Number of Clusters:", k, "\tAverage Distortion:", distortion)

# Creates a line plot to visualize the relationship between the number of clusters and average distortions
plt.plot(clusters, meanDistortions, "bx-")
plt.xlabel("k")
plt.ylabel("Average Distortion")
plt.title("Selecting k with the Elbow Method", fontsize=20)

# The plot will be displayed
plt.show()

Number of Clusters: 1 	Average Distortion: 2.5425069919221697
Number of Clusters: 2 	Average Distortion: 2.382318498894466
Number of Clusters: 3 	Average Distortion: 2.267596864267437
Number of Clusters: 4 	Average Distortion: 2.178151429073042
Number of Clusters: 5 	Average Distortion: 2.1277144764991163
Number of Clusters: 6 	Average Distortion: 2.088786281986941
Number of Clusters: 7 	Average Distortion: 2.0383149185355345
Number of Clusters: 8 	Average Distortion: 1.9907445164698618
Number of Clusters: 9 	Average Distortion: 1.8957432837183497
Number of Clusters: 10 	Average Distortion: 1.8665012362102018


#fit KMeans model and use visualizaer to indicate optimal K value
model = KMeans(random_state=42)
visualizer = KElbowVisualizer(model, k=(1, 11), timings=True)
visualizer.fit(k_means_df)  # fit the data to the visualizer
visualizer.show()  # finalize and render figure
plt.show()


# Creating an empty list to store silhouette scores
sil_score = []

# Define a range of cluster numbers to try (from 2 to 14 clusters)
cluster_list = range(2, 15)

# Will iterate through the cluster numbers
for n_clusters in cluster_list:
    # Creating a KMeans clustering model with the current number of clusters
    clusterer = KMeans(n_clusters=n_clusters, random_state=1)
    
    # Fit the KMeans model to the data in 'k_means_df' and obtain cluster assignments
    preds = clusterer.fit_predict(k_means_df)
    
    # Calculating the silhouette score for the current clustering
    score = silhouette_score(k_means_df, preds)
    
    # Will append the silhouette score to the 'sil_score' list
    sil_score.append(score)
    
    # Will print the silhouette score for the current number of clusters
    print("For n_clusters = {}, the silhouette score is {})".format(n_clusters, score))

# Creates a line plot to visualize the relationship between the number of clusters and silhouette scores
plt.plot(cluster_list, sil_score)

# Plot will be displayed
plt.show()

For n_clusters = 2, the silhouette score is 0.43969639509980457)
For n_clusters = 3, the silhouette score is 0.4644405674779404)
For n_clusters = 4, the silhouette score is 0.45434371948348606)
For n_clusters = 5, the silhouette score is 0.43169988466492354)
For n_clusters = 6, the silhouette score is 0.36698524210914063)
For n_clusters = 7, the silhouette score is 0.10587349598463779)
For n_clusters = 8, the silhouette score is 0.40021596082234684)
For n_clusters = 9, the silhouette score is 0.4051539505522535)
For n_clusters = 10, the silhouette score is 0.1147132918355368)
For n_clusters = 11, the silhouette score is 0.16268826704862682)
For n_clusters = 12, the silhouette score is 0.12883008320005324)
For n_clusters = 13, the silhouette score is 0.1744804713048242)
For n_clusters = 14, the silhouette score is 0.17597674098501367)


model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(2, 15), metric="silhouette", timings=True)
visualizer.fit(k_means_df)  # fit the data to the visualizer
visualizer.show()  # finalize and render figure

<Axes: title={'center': 'Silhouette Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='silhouette score'>


# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(5, random_state=1))  ## Complete the code to visualize the silhouette scores for certain number of clusters
visualizer.fit(k_means_df)
visualizer.show();


#create kmeans cluster model
kmeans = KMeans(n_clusters=5, random_state=42)

#fit model to scaled dataset
kmeans.fit(k_means_df)

KMeans(n_clusters=5, random_state=42)

KMeans(n_clusters=5, random_state=42)


# creating a copy of the original data
td1 = td.copy()

# adding kmeans cluster labels to the original and scaled dataframes
k_means_df["KM_segments"] = kmeans.labels_
td1["KM_segments"] = kmeans.labels_


km_clst_prof = td1.groupby("KM_segments").mean()
#add counts for number of stocks in every cluster
km_clst_prof["Count"] = (
    td1.groupby("KM_segments")["Current Price"].count().values
)


km_clst_prof.style.highlight_max(color="lightgreen", axis=0)


# will generate the names of the companies in each cluster
for cl in td1["KM_segments"].unique():
    print("In cluster {}, the following companies are present:".format(cl))
    print(td1[td1["KM_segments"] == cl]["Security"].unique().to_list())
    print()

In cluster 0, the following companies are present:
['American Airlines Group', 'AbbVie', 'Abbott Laboratories', 'Archer-Daniels-Midland Co', 'Ameren Corp', 'American Electric Power', 'AFLAC Inc', 'American International Group, Inc.', 'Apartment Investment & Mgmt', 'Assurant Inc', 'Arthur J. Gallagher & Co.', 'Akamai Technologies Inc', 'Albemarle Corp', 'Alaska Air Group Inc', 'Allstate Corp', 'Allegion', 'AMETEK Inc', 'Affiliated Managers Group Inc', 'Ameriprise Financial', 'American Tower Corp A', 'AutoNation Inc', 'Anthem Inc.', 'Aon plc', 'Amphenol Corp', 'Arconic Inc', 'Activision Blizzard', 'AvalonBay Communities, Inc.', 'American Water Works Company Inc', 'American Express Co', 'Boeing Company', 'Baxter International Inc.', 'BB&T Corporation', 'Bard (C.R.) Inc.', 'BIOGEN IDEC Inc.', 'The Bank of New York Mellon Corp.', 'Ball Corp', 'Bristol-Myers Squibb', 'Boston Scientific', 'BorgWarner', 'Boston Properties', 'Caterpillar Inc.', 'Chubb Limited', 'CBRE Group', 'Crown Castle International Corp.', 'Carnival Corp.', 'CF Industries Holdings Inc', 'Citizens Financial Group', 'Church & Dwight', 'C. H. Robinson Worldwide', 'Charter Communications', 'CIGNA Corp.', 'Cincinnati Financial', 'Colgate-Palmolive', 'Comerica Inc.', 'CME Group Inc.', 'Cummins Inc.', 'CMS Energy', 'Centene Corporation', 'CenterPoint Energy', 'Capital One Financial', 'The Cooper Companies', 'CSX Corp.', 'CenturyLink Inc', 'Cognizant Technology Solutions', 'Citrix Systems', 'CVS Health', 'Chevron Corp.', 'Dominion Resources', 'Delta Air Lines', 'Du Pont (E.I.)', 'Deere & Co.', 'Discover Financial Services', 'Quest Diagnostics', 'Danaher Corp.', 'The Walt Disney Company', 'Discovery Communications-A', 'Discovery Communications-C', 'Delphi Automotive', 'Digital Realty Trust', 'Dun & Bradstreet', 'Dover Corp.', 'Dr Pepper Snapple Group', 'Duke Energy', 'DaVita Inc.', 'Ecolab Inc.', 'Consolidated Edison', 'Equifax Inc.', "Edison Int'l", 'Eastman Chemical', 'Equity Residential', 'Eversource Energy', 'Essex Property Trust, Inc.', 'E*Trade', 'Eaton Corporation', 'Entergy Corp.', 'Exelon Corp.', "Expeditors Int'l", 'Expedia Inc.', 'Extra Space Storage', 'Fastenal Co', 'Fortune Brands Home & Security', 'FirstEnergy Corp', 'Fidelity National Information Services', 'Fiserv Inc', 'FLIR Systems', 'Fluor Corp.', 'Flowserve Corporation', 'FMC Corporation', 'Federal Realty Investment Trust', 'General Dynamics', 'General Growth Properties Inc.', 'Corning Inc.', 'General Motors', 'Genuine Parts', 'Garmin Ltd.', 'Goodyear Tire & Rubber', 'Grainger (W.W.) Inc.', 'Hasbro Inc.', 'Huntington Bancshares', 'HCA Holdings', 'Welltower Inc.', 'HCP Inc.', 'Hartford Financial Svc.Gp.', 'Harley-Davidson', "Honeywell Int'l Inc.", 'HP Inc.', 'Hormel Foods Corp.', 'Henry Schein', 'Host Hotels & Resorts', 'The Hershey Company', 'Humana Inc.', 'International Business Machines', 'IDEXX Laboratories', 'Intl Flavors & Fragrances', 'International Paper', 'Interpublic Group', 'Iron Mountain Incorporated', 'Illinois Tool Works', 'Invesco Ltd.', 'J. B. Hunt Transport Services', 'Jacobs Engineering Group', 'Juniper Networks', 'Kimco Realty', 'Kimberly-Clark', 'Kansas City Southern', 'Leggett & Platt', 'Lennar Corp.', 'Laboratory Corp. of America Holding', 'LKQ Corporation', 'L-3 Communications Holdings', 'Lilly (Eli) & Co.', 'Lockheed Martin Corp.', 'Alliant Energy Corp', 'Leucadia National Corp.', 'Southwest Airlines', 'Level 3 Communications', 'LyondellBasell', 'Mastercard Inc.', 'Mid-America Apartments', 'Macerich', "Marriott Int'l.", 'Masco Corp.', 'Mattel Inc.', "Moody's Corp", 'Mondelez International', 'MetLife Inc.', 'Mohawk Industries', 'Mead Johnson', 'McCormick & Co.', 'Martin Marietta Materials', 'Marsh & McLennan', '3M Company', 'Altria Group Inc', 'The Mosaic Company', 'Marathon Petroleum', 'Merck & Co.', 'M&T Bank Corp.', 'Mettler Toledo', 'Mylan N.V.', 'Navient', 'NASDAQ OMX Group', 'NextEra Energy', 'Nielsen Holdings', 'Norfolk Southern Corp.', 'Northern Trust Corp.', 'Nucor Corp.', 'Newell Brands', 'Realty Income Corporation', 'Omnicom Group', "O'Reilly Automotive", "People's United Financial", 'Pitney-Bowes', 'PACCAR Inc.', 'PG&E Corp.', 'Public Serv. Enterprise Inc.', 'PepsiCo Inc.', 'Principal Financial Group', 'Procter & Gamble', 'Progressive Corp.', 'Pulte Homes Inc.', 'Philip Morris International', 'PNC Financial Services', 'Pentair Ltd.', 'Pinnacle West Capital', 'PPG Industries', 'PPL Corp.', 'Prudential Financial', 'Phillips 66', 'Praxair Inc.', 'PayPal', 'Ryder System', 'Royal Caribbean Cruises Ltd', 'Robert Half International', 'Roper Industries', 'Republic Services Inc', 'SCANA Corp', 'Charles Schwab Corporation', 'Spectra Energy Corp.', 'Sealed Air', 'Sherwin-Williams', 'SL Green Realty', 'Scripps Networks Interactive Inc.', 'Southern Co.', 'Simon Property Group Inc', 'S&P Global, Inc.', 'Stericycle Inc', 'Sempra Energy', 'SunTrust Banks', 'State Street Corp.', 'Synchrony Financial', 'Stryker Corp.', 'Molson Coors Brewing Company', 'Tegna, Inc.', 'Torchmark Corp.', 'Thermo Fisher Scientific', 'The Travelers Companies Inc.', 'Tractor Supply Company', 'Tyson Foods', 'Tesoro Petroleum Co.', 'Total System Services', 'Texas Instruments', 'Under Armour', 'United Continental Holdings', 'UDR Inc', 'Universal Health Services, Inc.', 'United Health Group Inc.', 'Unum Group', 'Union Pacific', 'United Parcel Service', 'United Technologies', 'Varian Medical Systems', 'Valero Energy', 'Vulcan Materials', 'Vornado Realty Trust', 'Verisk Analytics', 'Verisign Inc.', 'Ventas Inc', 'Wec Energy Group Inc', 'Whirlpool Corp.', 'Waste Management Inc.', 'Western Union Co', 'Weyerhaeuser Corp.', 'Wyndham Worldwide', 'Xcel Energy Inc', 'XL Capital', 'Dentsply Sirona', 'Xerox Corp.', 'Xylem Inc.', 'Yum! Brands Inc', 'Zimmer Biomet Holdings', 'Zions Bancorp', 'Zoetis']

In cluster 2, the following companies are present:
['Adobe Systems Inc', 'Analog Devices, Inc.', 'Alexion Pharmaceuticals', 'Applied Materials Inc', 'Amgen Inc', 'Broadcom', 'Bank of America Corp', 'Celgene Corp.', 'eBay Inc.', 'Equinix', 'Edwards Lifesciences', 'Facebook', 'First Solar Inc', 'Frontier Communications', 'Halliburton Co.', "McDonald's Corp.", 'Monster Beverage', 'Newmont Mining Corp. (Hldg. Co.)', 'Skyworks Solutions', 'TripAdvisor', 'Vertex Pharmaceuticals Inc', 'Waters Corporation', 'Wynn Resorts Ltd', 'Yahoo Inc.']

In cluster 1, the following companies are present:
['Alliance Data Systems', 'Amazon.com Inc', 'Chipotle Mexican Grill', 'Intuitive Surgical Inc.', 'Priceline.com Inc', 'Regeneron']

In cluster 3, the following companies are present:
['Apache Corporation', 'Anadarko Petroleum Corp', 'Baker Hughes Inc', 'Chesapeake Energy', 'Cabot Oil & Gas', 'Concho Resources', 'Devon Energy Corp.', 'EOG Resources', 'EQT Corporation', 'Freeport-McMoran Cp & Gld', 'Hess Corporation', 'Hewlett Packard Enterprise', 'Kinder Morgan', 'Marathon Oil Corp.', 'Murphy Oil', 'Noble Energy Inc', 'Netflix Inc.', 'Newfield Exploration Co', 'National Oilwell Varco Inc.', 'ONEOK', 'Occidental Petroleum', 'Quanta Services Inc.', 'Range Resources Corp.', 'Southwestern Energy', 'Teradata Corp.', 'Williams Cos.', 'Cimarex Energy']

In cluster 4, the following companies are present:
['Citigroup Inc.', 'Ford Motor', 'Gilead Sciences', 'Intel Corp.', 'JPMorgan Chase & Co.', 'Coca Cola Company', 'Pfizer Inc.', 'AT&T Inc', 'Verizon Communications', 'Wells Fargo', 'Exxon Mobil Corp.']


#Generates the number of stocks associated with all the clusters
for k in range(0,td1['KM_segments'].nunique()):
    print('Number of stocks within each GICS Sector for Cluster '+str(k)+' are:')
    print(td1[td1['KM_segments']==k]['GICS Sector'].value_counts())
    print("   ")

Number of stocks within each GICS Sector for Cluster 0 are:
Industrials                    52
Financials                     45
Consumer Discretionary         33
Health Care                    30
Real Estate                    26
Utilities                      24
Information Technology         19
Materials                      18
Consumer Staples               17
Energy                          6
Telecommunications Services     2
Name: GICS Sector, dtype: int64
   
Number of stocks within each GICS Sector for Cluster 1 are:
Consumer Discretionary         3
Health Care                    2
Information Technology         1
Consumer Staples               0
Energy                         0
Financials                     0
Industrials                    0
Materials                      0
Real Estate                    0
Telecommunications Services    0
Utilities                      0
Name: GICS Sector, dtype: int64
   
Number of stocks within each GICS Sector for Cluster 2 are:
Information Technology         9
Health Care                    6
Consumer Discretionary         3
Consumer Staples               1
Energy                         1
Financials                     1
Materials                      1
Real Estate                    1
Telecommunications Services    1
Industrials                    0
Utilities                      0
Name: GICS Sector, dtype: int64
   
Number of stocks within each GICS Sector for Cluster 3 are:
Energy                         22
Information Technology          3
Industrials                     1
Materials                       1
Consumer Discretionary          0
Consumer Staples                0
Financials                      0
Health Care                     0
Real Estate                     0
Telecommunications Services     0
Utilities                       0
Name: GICS Sector, dtype: int64
   
Number of stocks within each GICS Sector for Cluster 4 are:
Financials                     3
Health Care                    2
Telecommunications Services    2
Consumer Discretionary         1
Consumer Staples               1
Energy                         1
Information Technology         1
Industrials                    0
Materials                      0
Real Estate                    0
Utilities                      0
Name: GICS Sector, dtype: int64


fig, axes = plt.subplots(3, 4, figsize=(20, 20))
store = 0

for j in range(3):
    for i in range(4):
        if store < 11:
            # Will generate a boxplot on the current subplot (axes[j][i])
            # Data is taken from 'td1'
            # The 'y' variable is selected dynamically based on 'store'
            # 'x' is set to "KM_segments" for all subplots
            # Use the "bright" color palette for the plot
            sns.boxplot(
                ax=axes[j][i],
                data=td1,
                y=td1.columns[3+store],
                x="KM_segments",
                palette="bright"
            )
            store = store + 1

fig.tight_layout(pad=3.0)


# list of distance metrics
distance_metrics = ["euclidean", "chebyshev", "mahalanobis", "cityblock"]

# list of linkage methods
linkage_methods = ["single", "complete", "average", "weighted"]

high_cophenet_corr = 0
high_dm_lm = [0, 0]

for dm in distance_metrics:
    for lm in linkage_methods:
        Z = linkage(k_means_df, metric=dm, method=lm)
        c, coph_dists = cophenet(Z, pdist(k_means_df))
        print(
            "Cophenetic correlation for {} distance and {} linkage is {}.".format(
                dm.capitalize(), lm, round(c,4)
            )
        )
        print(" ")
        if high_cophenet_corr < c:
            high_cophenet_corr = c
            high_dm_lm[0] = dm
            high_dm_lm[1] = lm

Cophenetic correlation for Euclidean distance and single linkage is 0.9436.
 
Cophenetic correlation for Euclidean distance and complete linkage is 0.8911.
 
Cophenetic correlation for Euclidean distance and average linkage is 0.9478.
 
Cophenetic correlation for Euclidean distance and weighted linkage is 0.771.
 
Cophenetic correlation for Chebyshev distance and single linkage is 0.9302.
 
Cophenetic correlation for Chebyshev distance and complete linkage is 0.7845.
 
Cophenetic correlation for Chebyshev distance and average linkage is 0.93.
 
Cophenetic correlation for Chebyshev distance and weighted linkage is 0.9046.
 
Cophenetic correlation for Mahalanobis distance and single linkage is 0.9347.
 
Cophenetic correlation for Mahalanobis distance and complete linkage is 0.8741.
 
Cophenetic correlation for Mahalanobis distance and average linkage is 0.9498.
 
Cophenetic correlation for Mahalanobis distance and weighted linkage is 0.9045.
 
Cophenetic correlation for Cityblock distance and single linkage is 0.9464.
 
Cophenetic correlation for Cityblock distance and complete linkage is 0.8516.
 
Cophenetic correlation for Cityblock distance and average linkage is 0.9197.
 
Cophenetic correlation for Cityblock distance and weighted linkage is 0.8724.


#will generate the combination of distance metric and linkage method with the maximum cophenetic correlation
print(
    "Maximum cophenetic correlation is {}, which is obtained with {} linkage.".format(
        round(high_cophenet_corr,4), high_dm_lm[1]))

Maximum cophenetic correlation is 0.9498, which is obtained with average linkage.


#List of linkage methods

linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"]

high_cophenet_corr = 0
high_dm_lm = [0, 0]

for lm in linkage_methods:
    Z = linkage(k_means_df, metric="euclidean", method=lm)
    c, coph_dists = cophenet(Z, pdist(k_means_df))
    print(
            "Cophenetic correlation for Euclidean distance and {} linkage is {}.".format(
                lm, round(c,4)
            )
        )
    print(" ")
    if high_cophenet_corr < c:
        high_cophenet_corr = c
        high_dm_lm[0] = "euclidean"
        high_dm_lm[1] = lm

Cophenetic correlation for Euclidean distance and single linkage is 0.9436.
 
Cophenetic correlation for Euclidean distance and complete linkage is 0.8911.
 
Cophenetic correlation for Euclidean distance and average linkage is 0.9478.
 
Cophenetic correlation for Euclidean distance and centroid linkage is 0.9371.
 
Cophenetic correlation for Euclidean distance and ward linkage is 0.7478.
 
Cophenetic correlation for Euclidean distance and weighted linkage is 0.771.


# list of linkage methods
linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"]

# lists to save results of cophenetic correlation calculation
compare_cols = ["Linkage", "Cophenetic Coefficient"]
compare=[]
# to create a subplot image
fig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))

# We will enumerate through the list of linkage methods above
# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation
for i, method in enumerate(linkage_methods):
    Z = linkage(k_means_df, metric="euclidean", method=method)

    dendrogram(Z, ax=axs[i])
    axs[i].set_title(f"Dendrogram ({method.capitalize()} Linkage)")

    coph_corr, coph_dist = cophenet(Z, pdist(k_means_df))
    axs[i].annotate(
        f"Cophenetic\nCorrelation\n{coph_corr:0.2f}",
        (0.80, 0.80),
        xycoords="axes fraction",
    )

    compare.append([method, coph_corr])


# Will generate a dataframe  dataframe to compare cophenetic correlations for different linkage methods
df_cc = pd.DataFrame(compare, columns=compare_cols)
df_cc = df_cc.sort_values(by="Cophenetic Coefficient")
df_cc


Z = linkage(k_means_df, metric='euclidean', method='average')
c, coph_dists = cophenet(Z , pdist(k_means_df))


# Create an instance of the AgglomerativeClustering model with specified parameters:
hierarchy = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='average')

# Fit the AgglomerativeClustering model to the data in 'k_means_df'.
# This process will group the data points into clusters based on the specified parameters.
hierarchy.fit(k_means_df)

AgglomerativeClustering(affinity='euclidean', linkage='average', n_clusters=5)

AgglomerativeClustering(affinity='euclidean', linkage='average', n_clusters=5)


# Create a copy of the DataFrame 'td1' and store it in the variable 'td_hierarchy'
td_hierarchy = td1.copy()

# Drop the column 'KM_segments' from the DataFrame 'td_hierarchy'
# The 'axis=1' argument specifies that we are dropping a column, and 'inplace=True' means the change is applied to the DataFrame itself
td_hierarchy.drop("KM_segments", axis=1, inplace=True)

# Add a new column 'HC_clusters' to the DataFrame 'td_hierarchy'
# The values for this column are assigned based on the cluster labels obtained from some hierarchical clustering object or model referred to as 'hierarchy'
td_hierarchy['HC_clusters'] = hierarchy.labels_


#group dataset by Hierarchical clusters
cluster_profile_td = td_hierarchy.groupby("HC_clusters").mean()


#add counts for number of stocks in each cluster
cluster_profile_td["Count"] = (
    td_hierarchy.groupby("HC_clusters")["Current Price"].count().values)


#show dataframe with maximum values for each metric highlighted
cluster_profile_td.style.highlight_max(color="lightgreen", axis=0)


# Iterate through unique values in the 'HC_clusters' column
for cl in td_hierarchy["HC_clusters"].unique():
    # Print a header indicating the current cluster
    print("In cluster {}, the following companies are present:".format(cl))
    
    # Select unique company names within the current cluster and convert them to a list
    company_list = td_hierarchy[td_hierarchy["HC_clusters"] == cl]["Security"].unique().tolist()
    
    # Prints the list of companies in the current cluster
    print(company_list)
    
    # Prints an empty line for separation between clusters
    print()

In cluster 0, the following companies are present:
['American Airlines Group', 'AbbVie', 'Abbott Laboratories', 'Adobe Systems Inc', 'Analog Devices, Inc.', 'Archer-Daniels-Midland Co', 'Alliance Data Systems', 'Ameren Corp', 'American Electric Power', 'AFLAC Inc', 'American International Group, Inc.', 'Apartment Investment & Mgmt', 'Assurant Inc', 'Arthur J. Gallagher & Co.', 'Akamai Technologies Inc', 'Albemarle Corp', 'Alaska Air Group Inc', 'Allstate Corp', 'Allegion', 'Alexion Pharmaceuticals', 'Applied Materials Inc', 'AMETEK Inc', 'Affiliated Managers Group Inc', 'Amgen Inc', 'Ameriprise Financial', 'American Tower Corp A', 'AutoNation Inc', 'Anthem Inc.', 'Aon plc', 'Anadarko Petroleum Corp', 'Amphenol Corp', 'Arconic Inc', 'Activision Blizzard', 'AvalonBay Communities, Inc.', 'Broadcom', 'American Water Works Company Inc', 'American Express Co', 'Boeing Company', 'Bank of America Corp', 'Baxter International Inc.', 'BB&T Corporation', 'Bard (C.R.) Inc.', 'Baker Hughes Inc', 'BIOGEN IDEC Inc.', 'The Bank of New York Mellon Corp.', 'Ball Corp', 'Bristol-Myers Squibb', 'Boston Scientific', 'BorgWarner', 'Boston Properties', 'Citigroup Inc.', 'Caterpillar Inc.', 'Chubb Limited', 'CBRE Group', 'Crown Castle International Corp.', 'Carnival Corp.', 'Celgene Corp.', 'CF Industries Holdings Inc', 'Citizens Financial Group', 'Church & Dwight', 'C. H. Robinson Worldwide', 'Charter Communications', 'CIGNA Corp.', 'Cincinnati Financial', 'Colgate-Palmolive', 'Comerica Inc.', 'CME Group Inc.', 'Chipotle Mexican Grill', 'Cummins Inc.', 'CMS Energy', 'Centene Corporation', 'CenterPoint Energy', 'Capital One Financial', 'Cabot Oil & Gas', 'The Cooper Companies', 'CSX Corp.', 'CenturyLink Inc', 'Cognizant Technology Solutions', 'Citrix Systems', 'CVS Health', 'Chevron Corp.', 'Concho Resources', 'Dominion Resources', 'Delta Air Lines', 'Du Pont (E.I.)', 'Deere & Co.', 'Discover Financial Services', 'Quest Diagnostics', 'Danaher Corp.', 'The Walt Disney Company', 'Discovery Communications-A', 'Discovery Communications-C', 'Delphi Automotive', 'Digital Realty Trust', 'Dun & Bradstreet', 'Dover Corp.', 'Dr Pepper Snapple Group', 'Duke Energy', 'DaVita Inc.', 'Devon Energy Corp.', 'eBay Inc.', 'Ecolab Inc.', 'Consolidated Edison', 'Equifax Inc.', "Edison Int'l", 'Eastman Chemical', 'EOG Resources', 'Equinix', 'Equity Residential', 'EQT Corporation', 'Eversource Energy', 'Essex Property Trust, Inc.', 'E*Trade', 'Eaton Corporation', 'Entergy Corp.', 'Edwards Lifesciences', 'Exelon Corp.', "Expeditors Int'l", 'Expedia Inc.', 'Extra Space Storage', 'Ford Motor', 'Fastenal Co', 'Fortune Brands Home & Security', 'Freeport-McMoran Cp & Gld', 'FirstEnergy Corp', 'Fidelity National Information Services', 'Fiserv Inc', 'FLIR Systems', 'Fluor Corp.', 'Flowserve Corporation', 'FMC Corporation', 'Federal Realty Investment Trust', 'First Solar Inc', 'Frontier Communications', 'General Dynamics', 'General Growth Properties Inc.', 'Gilead Sciences', 'Corning Inc.', 'General Motors', 'Genuine Parts', 'Garmin Ltd.', 'Goodyear Tire & Rubber', 'Grainger (W.W.) Inc.', 'Halliburton Co.', 'Hasbro Inc.', 'Huntington Bancshares', 'HCA Holdings', 'Welltower Inc.', 'HCP Inc.', 'Hess Corporation', 'Hartford Financial Svc.Gp.', 'Harley-Davidson', "Honeywell Int'l Inc.", 'Hewlett Packard Enterprise', 'HP Inc.', 'Hormel Foods Corp.', 'Henry Schein', 'Host Hotels & Resorts', 'The Hershey Company', 'Humana Inc.', 'International Business Machines', 'IDEXX Laboratories', 'Intl Flavors & Fragrances', 'Intel Corp.', 'International Paper', 'Interpublic Group', 'Iron Mountain Incorporated', 'Intuitive Surgical Inc.', 'Illinois Tool Works', 'Invesco Ltd.', 'J. B. Hunt Transport Services', 'Jacobs Engineering Group', 'Juniper Networks', 'JPMorgan Chase & Co.', 'Kimco Realty', 'Kimberly-Clark', 'Kinder Morgan', 'Coca Cola Company', 'Kansas City Southern', 'Leggett & Platt', 'Lennar Corp.', 'Laboratory Corp. of America Holding', 'LKQ Corporation', 'L-3 Communications Holdings', 'Lilly (Eli) & Co.', 'Lockheed Martin Corp.', 'Alliant Energy Corp', 'Leucadia National Corp.', 'Southwest Airlines', 'Level 3 Communications', 'LyondellBasell', 'Mastercard Inc.', 'Mid-America Apartments', 'Macerich', "Marriott Int'l.", 'Masco Corp.', 'Mattel Inc.', "McDonald's Corp.", "Moody's Corp", 'Mondelez International', 'MetLife Inc.', 'Mohawk Industries', 'Mead Johnson', 'McCormick & Co.', 'Martin Marietta Materials', 'Marsh & McLennan', '3M Company', 'Monster Beverage', 'Altria Group Inc', 'The Mosaic Company', 'Marathon Petroleum', 'Merck & Co.', 'Marathon Oil Corp.', 'M&T Bank Corp.', 'Mettler Toledo', 'Murphy Oil', 'Mylan N.V.', 'Navient', 'Noble Energy Inc', 'NASDAQ OMX Group', 'NextEra Energy', 'Newmont Mining Corp. (Hldg. Co.)', 'Netflix Inc.', 'Newfield Exploration Co', 'Nielsen Holdings', 'National Oilwell Varco Inc.', 'Norfolk Southern Corp.', 'Northern Trust Corp.', 'Nucor Corp.', 'Newell Brands', 'Realty Income Corporation', 'ONEOK', 'Omnicom Group', "O'Reilly Automotive", 'Occidental Petroleum', "People's United Financial", 'Pitney-Bowes', 'PACCAR Inc.', 'PG&E Corp.', 'Public Serv. Enterprise Inc.', 'PepsiCo Inc.', 'Pfizer Inc.', 'Principal Financial Group', 'Procter & Gamble', 'Progressive Corp.', 'Pulte Homes Inc.', 'Philip Morris International', 'PNC Financial Services', 'Pentair Ltd.', 'Pinnacle West Capital', 'PPG Industries', 'PPL Corp.', 'Prudential Financial', 'Phillips 66', 'Quanta Services Inc.', 'Praxair Inc.', 'PayPal', 'Ryder System', 'Royal Caribbean Cruises Ltd', 'Regeneron', 'Robert Half International', 'Roper Industries', 'Range Resources Corp.', 'Republic Services Inc', 'SCANA Corp', 'Charles Schwab Corporation', 'Spectra Energy Corp.', 'Sealed Air', 'Sherwin-Williams', 'SL Green Realty', 'Scripps Networks Interactive Inc.', 'Southern Co.', 'Simon Property Group Inc', 'S&P Global, Inc.', 'Stericycle Inc', 'Sempra Energy', 'SunTrust Banks', 'State Street Corp.', 'Skyworks Solutions', 'Southwestern Energy', 'Synchrony Financial', 'Stryker Corp.', 'AT&T Inc', 'Molson Coors Brewing Company', 'Teradata Corp.', 'Tegna, Inc.', 'Torchmark Corp.', 'Thermo Fisher Scientific', 'TripAdvisor', 'The Travelers Companies Inc.', 'Tractor Supply Company', 'Tyson Foods', 'Tesoro Petroleum Co.', 'Total System Services', 'Texas Instruments', 'Under Armour', 'United Continental Holdings', 'UDR Inc', 'Universal Health Services, Inc.', 'United Health Group Inc.', 'Unum Group', 'Union Pacific', 'United Parcel Service', 'United Technologies', 'Varian Medical Systems', 'Valero Energy', 'Vulcan Materials', 'Vornado Realty Trust', 'Verisk Analytics', 'Verisign Inc.', 'Vertex Pharmaceuticals Inc', 'Ventas Inc', 'Verizon Communications', 'Waters Corporation', 'Wec Energy Group Inc', 'Wells Fargo', 'Whirlpool Corp.', 'Waste Management Inc.', 'Williams Cos.', 'Western Union Co', 'Weyerhaeuser Corp.', 'Wyndham Worldwide', 'Wynn Resorts Ltd', 'Cimarex Energy', 'Xcel Energy Inc', 'XL Capital', 'Exxon Mobil Corp.', 'Dentsply Sirona', 'Xerox Corp.', 'Xylem Inc.', 'Yahoo Inc.', 'Yum! Brands Inc', 'Zimmer Biomet Holdings', 'Zions Bancorp', 'Zoetis']

In cluster 3, the following companies are present:
['Amazon.com Inc']

In cluster 2, the following companies are present:
['Apache Corporation', 'Chesapeake Energy']

In cluster 4, the following companies are present:
['Facebook']

In cluster 1, the following companies are present:
['Priceline.com Inc']


# Loop through the range of unique values in the 'HC_clusters' column
for k in range(0, td_hierarchy['HC_clusters'].nunique()):
    # Prints a header indicating the current cluster
    print('The number of stocks within each GICS Sector for Cluster ' + str(k) + ' are:')
    
    # Calculate and print the count of stocks in each GICS Sector for the current cluster
    print(td_hierarchy[td_hierarchy['HC_clusters'] == k]['GICS Sector'].value_counts())
    
    # Prints an empty line for separation between clusters
    print("   ")

The number of stocks within each GICS Sector for Cluster 0 are:
Industrials                    53
Financials                     49
Health Care                    40
Consumer Discretionary         38
Information Technology         32
Energy                         28
Real Estate                    27
Utilities                      24
Materials                      20
Consumer Staples               19
Telecommunications Services     5
Name: GICS Sector, dtype: int64
   
The number of stocks within each GICS Sector for Cluster 1 are:
Consumer Discretionary         1
Consumer Staples               0
Energy                         0
Financials                     0
Health Care                    0
Industrials                    0
Information Technology         0
Materials                      0
Real Estate                    0
Telecommunications Services    0
Utilities                      0
Name: GICS Sector, dtype: int64
   
The number of stocks within each GICS Sector for Cluster 2 are:
Energy                         2
Consumer Discretionary         0
Consumer Staples               0
Financials                     0
Health Care                    0
Industrials                    0
Information Technology         0
Materials                      0
Real Estate                    0
Telecommunications Services    0
Utilities                      0
Name: GICS Sector, dtype: int64
   
The number of stocks within each GICS Sector for Cluster 3 are:
Consumer Discretionary         1
Consumer Staples               0
Energy                         0
Financials                     0
Health Care                    0
Industrials                    0
Information Technology         0
Materials                      0
Real Estate                    0
Telecommunications Services    0
Utilities                      0
Name: GICS Sector, dtype: int64
   
The number of stocks within each GICS Sector for Cluster 4 are:
Information Technology         1
Consumer Discretionary         0
Consumer Staples               0
Energy                         0
Financials                     0
Health Care                    0
Industrials                    0
Materials                      0
Real Estate                    0
Telecommunications Services    0
Utilities                      0
Name: GICS Sector, dtype: int64


# Create a 3x4 grid of subplots with a specified figure size
fig, axes = plt.subplots(3, 4, figsize=(20, 20))

# Initialize a variable to keep track of the current plot
store = 0

# Loop through rows (j) and columns (i) of the grid
for j in range(3):
    for i in range(4):
        # Check if we haven't used all the columns (up to 11)
        if store < 11:
            # Create a boxplot on the current subplot (axes[j][i])
            # Data is taken from 'td_hierarchy'
            # The 'y' variable is selected dynamically based on 'store'
            # 'x' is set to "HC_clusters" for all subplots
            # Use the "bright" color palette for the plot
            sns.boxplot(
                ax=axes[j][i],
                data=td_hierarchy,
                y=td_hierarchy.columns[3 + store],
                x="HC_clusters",
                palette="bright"
            )
            # Increment the 'store' variable to move to the next column
            store = store + 1

# Adjust the layout of the subplots with some padding
fig.tight_layout(pad=3.0)

# Display the resulting plot
plt.show()

	Ticker Symbol	Security	GICS Sector	GICS Sub Industry	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio
102	DVN	Devon Energy Corp.	Energy	Oil & Gas Exploration & Production	32.00	-15.48	2.92	205	70	830000000	-14454000000	-35.55	406582278.50	93.09	1.79
125	FB	Facebook	Information Technology	Internet Software & Services	104.66	16.22	1.32	8	958	592000000	3669000000	1.31	2800763359.00	79.89	5.88
11	AIV	Apartment Investment & Mgmt	Real Estate	REITs	40.03	7.58	1.16	15	47	21818000	248710000	1.52	163625000.00	26.34	-1.27
248	PG	Procter & Gamble	Consumer Staples	Personal Products	79.41	10.66	0.81	17	129	160383000	636056000	3.28	491391569.00	24.07	-2.26
238	OXY	Occidental Petroleum	Energy	Oil & Gas Exploration & Production	67.61	0.87	1.59	32	64	-588000000	-7829000000	-10.23	765298142.70	93.09	3.35
336	YUM	Yum! Brands Inc	Consumer Discretionary	Restaurants	52.52	-8.70	1.48	142	27	159000000	1293000000	2.97	435353535.40	17.68	-3.84
112	EQT	EQT Corporation	Energy	Oil & Gas Exploration & Production	52.13	-21.25	2.36	2	201	523803000	85171000	0.56	152091071.40	93.09	9.57
147	HAL	Halliburton Co.	Energy	Oil & Gas Equipment & Services	34.04	-5.10	1.97	4	189	7786000000	-671000000	-0.79	849367088.60	93.09	17.35
89	DFS	Discover Financial Services	Financials	Consumer Finance	53.62	3.65	1.16	20	99	2288000000	2297000000	5.14	446887159.50	10.43	-0.38
173	IVZ	Invesco Ltd.	Financials	Asset Management & Custody Banks	33.48	7.07	1.58	12	67	412000000	968100000	2.26	428362831.90	14.81	4.22

	count	mean	std	min	25%	50%	75%	max
Current Price	340.00	80.86	98.06	4.50	38.55	59.70	92.88	1274.95
Price Change	340.00	4.08	12.01	-47.13	-0.94	4.82	10.70	55.05
Volatility	340.00	1.53	0.59	0.73	1.13	1.39	1.70	4.58
ROE	340.00	39.60	96.55	1.00	9.75	15.00	27.00	917.00
Cash Ratio	340.00	70.02	90.42	0.00	18.00	47.00	99.00	958.00
Net Cash Flow	340.00	55537620.59	1946365312.18	-11208000000.00	-193906500.00	2098000.00	169810750.00	20764000000.00
Net Income	340.00	1494384602.94	3940150279.33	-23528000000.00	352301250.00	707336000.00	1899000000.00	24442000000.00
Earnings Per Share	340.00	2.78	6.59	-61.20	1.56	2.90	4.62	50.09
Estimated Shares Outstanding	340.00	577028337.75	845849595.42	27672156.86	158848216.10	309675137.80	573117457.32	6159292035.00
P/E Ratio	340.00	32.61	44.35	2.94	15.04	20.82	31.76	528.04
P/B Ratio	340.00	-1.72	13.97	-76.12	-4.35	-1.07	3.92	129.06

	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio	Count
KM_segments
0	73.458985	4.908289	1.381480	34.996324	50.169118	-6010529.411765	1500021988.970588	3.702518	430274527.337537	23.661694	-3.534065	272
1	632.714991	7.374164	1.541343	19.333333	158.333333	-24046333.333333	907393166.666667	16.270000	125797901.323333	123.049240	35.355736	6
2	88.820556	14.960905	1.783471	24.791667	292.750000	1531729250.000000	1518241000.000000	2.060417	755930067.074167	54.351538	7.448500	24
3	38.099260	-15.370329	2.910500	107.074074	50.037037	-159428481.481481	-3887457740.740741	-9.473704	480398572.845926	90.619220	1.342067	27
4	50.517273	5.747586	1.130399	31.090909	75.909091	-1072272727.272727	14833090909.090910	4.154545	4298826628.727273	14.803577	-4.552119	11

	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio	Count
HC_clusters
0	75.787246	4.064498	1.515873	35.277612	67.182090	63325901.492537	1610410164.179105	2.910493	572636307.447164	30.845937	-1.779318	335
1	1274.949951	3.190527	1.268340	29.000000	184.000000	-1671386000.000000	2551360000.000000	50.090000	50935516.070000	25.453183	-1.052429	1
2	24.485001	-13.351992	3.482611	802.000000	51.000000	-1292500000.000000	-19106500000.000000	-41.815000	519573983.250000	60.748608	1.565141	2
3	675.890015	32.268105	1.460386	4.000000	58.000000	1333000000.000000	596000000.000000	1.280000	465625000.000000	528.039074	3.904430	1
4	104.660004	16.224320	1.320606	8.000000	958.000000	592000000.000000	3669000000.000000	1.310000	2800763359.000000	79.893133	5.884467	1

Unsupervised Learning: Trade&Ahead¶

Context¶

Objective¶

Data Dictionary¶

Importing necessary libraries and data¶

Data Overview¶

Exploratory Data Analysis (EDA)¶

Data Preprocessing¶

K-means Clustering¶

Hierarchical Clustering¶

Checking Dendrograms¶

K-means vs Hierarchical Clustering¶

Actionable Insights and Recommendations¶

	Linkage	Cophenetic Coefficient
4	ward	0.75
5	weighted	0.77
1	complete	0.89
3	centroid	0.94
0	single	0.94
2	average	0.95